A regression approach based on separability maximization for modeling a continuous-valued stress index from electrocardiogram data

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Rui Teixeira Ribeiro (Author) Other; João Paulo Silva Cunha (Author) FEUP

Journal

Title: Biomedical Signal Processing and Control

Vol. 46

Pages: 33-45

ISSN: 1746-8094

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 6 Citations

INSPEC

Other information

Authenticus ID: P-00P-17Z

DOI: 10.1016/j.bspc.2018.06.006

Abstract (EN): In this work we propose a regression approach based on separability maximization (RASMa) for modeling a continuous-valued estimate of the stress level (we called it stress index) using some features extracted from electrocardiogram (ECG) data. Since no objective measure of the actual stress level (output) is available, finding the stress index cannot be addressed as a classical regression problem. Instead, the proposed approach finds the linear combination of features that maximizes the separability of stress index values for non-stress and stress events. In short, RASMa combines linear discriminant analysis with the Bhattacharyya distance, embedded in a leave-one-subject-out cross-validation scheme. A 26-case pilot study using 17 heart rate variability (HRV) features was conducted as a proof of concept. A near real-time application tool for monitoring stress level over time was also implemented based on the model obtained from the pilot study.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13